
We compare the performance of CycleNet with baseline CNN and find that CycleNet performs better at image classification on CIFAR-10 and ImageNet ([1]}, [2]}), and approaches the performance of ResNet [3]} and MobileNet [4]} with a similar number of parameters.
On ImageNet, the best performance is obtained by a hybrid network including cycles of both standard and orthogonal convolutions.

We measure the receptive field size of activations at different depths using saliency maps [5]}, [6]}, and confirm that CycleNet achieves the maximum receptive field size after a single cycle, while the baseline CNN network requires a large number of layers.

We hypothesise that large receptive fields favour classification of images by shape rather than texture of objects, and we test transfer learning to Stylised ImageNet and Cue-Conflict datasets ([7]}).
While the best performance on ImageNet is obtained by a hybrid network, the best transfer to stylised data is obtained when all cycles have orthogonal convolutions, thus confirming the importance of large receptive fields.

Finally, we look into a task where CNNs are known to fail dramatically because of their short-range interactions: the Pathfinder challenge (Fig.REF b, [8]}).
We show that CycleNet outperforms the baseline CNN by a large margin in this task.

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